Final Project

World Data Analysis Project

Welcome to the World Data Analysis project! My primary objective is to navigate through the intricacies of global data, unraveling patterns and stories using a myriad of data visualization techniques. For this exploration, we have meticulously curated datasets from Kaggle, a leading platform for datasets and data science resources.

Datasets Overview

Our analytical journey is fueled by two datasets:

1. Countries of the World 2023 Dataset:
   • Source: Countries of the World 2023 Dataset
   • A detailed exploration of various aspects of countries in the year 2023, this dataset serves as a valuable resource for our analysis.
2. Population Collapse Dataset:
   • Source: Population Collapse Dataset
   • This dataset provides a comprehensive view of population dynamics, offering insights into trends and changes over time.

Additional Research Source

In addition to our curated datasets, we draw insights from a research article titled “Does tertiary education expansion affect the fertility of women past the college-entry age?” (Link to Research).

Aim

The overarching aim of this project is to conduct a comprehensive analysis of global data using advanced data visualization techniques. By harnessing diverse datasets obtained from Kaggle, we aim to uncover patterns, trends, and interrelationships within the data. Through a combination of statistical analysis and visual representation, our goal is to provide valuable insights into key aspects such as population dynamics, economic indicators, and socio-demographic factors.

library(tidyverse)
library(sf)
library(rnaturalearth)
library(viridis)
library(ggradar)
library(plotly)
library(rvest)
library(tm)
library(wordcloud)
library(quanteda)
library(readtext)
library(RColorBrewer)
library(wordcloud2)
library(hexbin)

# Read birth rate data 
time_birth_data <- read_csv("Downloads/Google Download/total_birth_rate.csv")

# Read death rate data 
time_death_data <- read_csv("Downloads/Google Download/total_death_rate.csv")

# Read life expectancy data 
time_le_data <- read_csv("Downloads/Google Download/total_Life_expectancy_at_birth.csv")

# Read world data for the year 2023 
world_data_2023 <- read_csv("Downloads/Google Download/world-data-2023.csv")

#retrieve world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Merge the world map data with the world data for the year 2023
merged_data <- merge(world, world_data_2023, by.x = "name", by.y = "Country", all.x = TRUE)

Countries of the World 2023 Dataset Description

Dataset basic info The world_data_2023 dataset include 195 rows (Countries) and 35 variables (columns), The used columns are listed below

Country: The name of the country. (Text)

Population: The total population of the country. (Numeric)

Official Language: The primary language recognized by the country. (Text)

GDP (Gross Domestic Product): The total economic output of the country. (Numeric)

Life Expectancy: The average expected lifespan of individuals in the country. (Numeric)

Minimum Wage: The minimum remuneration that employers are legally required to pay workers. (Numeric)

Unemployment Rate: The percentage of the workforce that is unemployed and actively seeking employment. (Numeric)

Gross Tertiary Education Enrollment (%): The percentage of the population enrolled in tertiary education. (Numeric)

Physicians per Thousand: The number of physicians per thousand people in the country. (Numeric)

Fertility Rate: The average number of children born to a woman over her lifetime. (Numeric)

Each row in the dataset represents a unique country, providing a comprehensive overview of various socio-economic and demographic factors. This dataset is valuable for conducting research related to global trends, economic analyses, and public health studies.

Population Collapse Dataset Description

Dataset basic info

The second dataset is a comprehensive amalgamation of multiple datasets utilized to construct meaningful timeline plots. This dataset incorporates information from nine distinct sources, of which three datasets—namely total_birth_rate, total_death_rate, and total_Life_expectancy_at_birth—have been selected for detailed analysis.

These chosen datasets encompass data spanning from the year 2000 to 2021, capturing vital statistics for 266 countries, including entities that may not be officially recognized by the United Nations.

1. Total Birth Rate: The total number of live births per 1,000 people in a given year.
2. Total Death Rate: The total number of deaths per 1,000 people in a given year.
3. Total Life Expectancy at Birth: The average number of years a newborn is expected to live, considering the mortality patterns existing at the time of their birth.

The temporal scope, coupled with the extensive coverage of countries, renders this dataset a valuable resource for conducting in-depth analyses of global demographic trends and life expectancy patterns over the specified time frame.

 #Data Cleaning and Conversion


# Clean and convert the 'GDP' column to numeric, removing non-numeric characters
merged_data$GDP <- as.numeric(gsub("[^0-9.]", "", merged_data$GDP))

# Clean and convert the 'Minimum wage' column to numeric, removing non-numeric characters
merged_data$`Minimum wage` <- as.numeric(gsub("[^0-9.]", "", merged_data$`Minimum wage`))

# Clean and convert the 'Life expectancy' column to numeric, removing non-numeric characters
merged_data$`Life expectancy` <- as.numeric(gsub("[^0-9.]", "", merged_data$`Life expectancy`))

# Clean and convert the 'Unemployment rate' column to numeric, removing non-numeric characters
merged_data$`Unemployment rate` <- as.numeric(gsub("[^0-9.]", "", merged_data$`Minimum wage`))


# Clean and convert the 'Gross tertiary education enrollment (%)' column to numeric, removing non-numeric characters
merged_data$`Gross tertiary education enrollment (%)` <- as.numeric(gsub("[^0-9.]", "", merged_data$`Gross tertiary education enrollment (%)`))

Unveiling Socio-Economic Dynamics: Exploring Life Expectancy, Minimum Wage, and Key Variables Across Nations

Introduction

In this section, we delve into the intricate relationship between life expectancy and minimum wage, along with other crucial variables such as unemployment rate, population, and GDP across various countries. The visualizations aim to unravel potential correlations and patterns.

Life Expectancy Choropleth Map

We start by creating a choropleth map to visualize life expectancy across different countries for the year 2023. The map dynamically represents life expectancy values, allowing for an interactive exploration.

color_limits <- c(50, 85) #based on min and max Life expectancy

# Create an interactive choropleth map
choropleth_map <- ggplot(merged_data, aes(fill = `Life expectancy`, group = name)) +
  geom_sf(color = "white", size = 0.2) +
  scale_fill_viridis_c(
    name = "Life expectancy",
    labels = scales::comma,
    option = "viridis",
    limits = color_limits,
    breaks = c(50, 60, 70, 80, 85),
    direction = -1  
  ) +
  labs(title = "Choropleth Map of Life expectancy 2023 (Dynamic)")

# Convert ggplot to plotly
interactive_choropleth_map <- ggplotly(choropleth_map)
interactive_choropleth_map

Minimum Wage Choropleth Map

A similar choropleth map is generated to illustrate the distribution of minimum wage values across countries in 2023.

# Set the limits and breaks for the color scale
color_limits <- c(0, 14) 

color_breaks <- seq(0, 14, by = 2)  

# Create an interactive choropleth map for Minimum Wage
choropleth_map_minimum_wage <- ggplot(merged_data, aes(fill = `Minimum wage`, group = name)) +
  geom_sf(color = "white", size = 0.2) +
  scale_fill_viridis_c(
    name = "Minimum wage",
    labels = scales::comma,
    option = "viridis",
    limits = color_limits,
    breaks = color_breaks,
    direction = -1
  ) +
  labs(title = "Choropleth Map of Minimum Wage 2023 (Dynamic)")

# Convert ggplot to plotly
interactive_choropleth_map_minimum_wage <- ggplotly(choropleth_map_minimum_wage)
interactive_choropleth_map_minimum_wage

Box Plot: Minimum Wage and Life Expectancy Relationship

To delve deeper into the relationship between minimum wage and life expectancy, a box plot is constructed. The plot groups life expectancy into ranges and provides a visual representation of how minimum wage varies within each group

# Filter out rows with NA in 'Life Expectancy'
merged_data_no_na <- merged_data[!is.na(merged_data$`Life expectancy`),]

# Create the box plot
box_plot <- ggplot(merged_data_no_na, aes(x = cut(`Life expectancy`, breaks = 10), y = `Minimum wage`, fill = `Life expectancy`)) +
  geom_boxplot(alpha = 0.7, color = "darkblue", size = 0.5, outlier.color = "red", outlier.shape = 16, outlier.size = 2) +
  scale_fill_viridis_c(option = "plasma") +
  theme_minimal() +
  labs(title = "Box Plot of Minimum Wage by Life Expectancy",
       x = "Life Expectancy (Grouped)",
       y = "Minimum Wage",
       fill = "Life Expectancy Group") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "bottom")

# Convert ggplot to plotly
interactive_box_plot <- ggplotly(box_plot)

# Show the interactive box plot
interactive_box_plot

The box plot reveals potential trends and variations in minimum wage based on life expectancy groups. It sets the stage for a more detailed exploration of the connection between these two variables.

Scatter Plot: Unemployment Rate’s Role

To enrich the narrative, we extend our analysis by incorporating the unemployment rate. A scatter plot is employed to visualize the relationships between minimum wage, life expectancy, and unemployment rate.

# Use ggplot2 to create a scatter plot
sc<-ggplot(merged_data, aes(x = `Life expectancy`, y = `Minimum wage`, color = `Unemployment rate`)) +
  geom_point(size = 3, alpha = 0.5) +
  scale_color_viridis_c(option = "plasma") +
  labs(title = "Scatter Plot of Minimum Wage vs. Life Expectancy",
       x = "Life Expectancy",
       y = "Minimum Wage",
       color = "Unemployment Rate")
# interactive it
wage_le <- ggplotly(sc)

wage_le

The scatter plot introduces the unemployment rate as a third variable. We observe how changes in minimum wage and life expectancy correlate with fluctuations in unemployment rate.

Bubble Chart: Comprehensive Overview

Building on the previous visualizations, we present a 3D bubble chart that incorporates minimum wage, GDP, life expectancy, and population (Points Size). This chart aims to provide a comprehensive overview, considering multiple factors simultaneously.

# Create Bubble Chart
bubble_chart <- plot_ly(
  data = merged_data,
  x = ~`Minimum wage`,
  y = ~GDP,
  z = ~`Life expectancy`,
  size = ~Population,  
  text = ~name,
  color = ~continent,
  marker = list(
    opacity = 0.8,
    colorscale = 'Viridis',
    sizemode = "diameter"
  ),
  type = "scatter3d",
  mode = "markers"
) 

# Explicitly set color and legend group
for (cont in unique(merged_data$continent)) {
  subset_data <- merged_data[merged_data$Continent == cont, ]
  bubble_chart <- add_trace(bubble_chart,
                            data = subset_data,
                            x = ~`Minimum wage`,
                            y = ~GDP,
                            z = ~`Life expectancy`,
                            size = ~Population,
                            text = ~name,
                            color = ~continent,
                            colorscale = 'Viridis',
                            sizemode = "diameter",
                            type = "scatter3d",
                            mode = "markers",
                            legendgroup = cont)
}

# Set layout
bubble_chart <- layout(bubble_chart,
                       scene = list(
                         xaxis = list(title = "Minimum wage"),
                         yaxis = list(title = "GDP"),
                         zaxis = list(title = "Life Expectancy")
                       ))

# Show the chart
bubble_chart

Conclusion

Our visual analysis supports a positive correlation between minimum wage and life expectancy across countries. The box plot illustrates how minimum wage varies within distinct life expectancy groups. The scatter plot introduces unemployment rate, showing a positive correlation with both minimum wage and life expectancy.

The comprehensive bubble chart adds another layer, considering GDP and population. Through this lens, we identify clusters of countries, reinforcing the intricate connections between minimum wage, life expectancy, and broader economic factors.

This exploration suggests that policies enhancing minimum wage may contribute to improved living standards and positively impact life expectancy. However, recognizing the complexity and potential influence of external factors is crucial.

Birth Rate and Its Influencing Factors

Introduction

In this exploration, we aim to uncover the intricate connections between birth rates and crucial socio-economic factors. Through various visualizations, our goal is to reveal potential patterns and correlations, offering valuable insights into the dynamics that shape birth rates. Let’s dive into the data and unravel the story it tells about the complex interplay of socio-economic elements influencing birth rates.

KDE Plot of Birth Rate vs. Minimum Wage

# Select relevant columns and drop NA values
birth_wage <- merged_data %>%
  select(`Birth Rate`, `Minimum wage`) %>%
  drop_na()

# KDE plot with filled contours and no legend
kde_plot <- ggplot(birth_wage, aes(x = `Birth Rate`, y = `Minimum wage`)) +
  geom_density_2d_filled(color = "white", bins = 20) +
  labs(title = "KDE Plot of Birth Rate vs. Minimum Wage",
       x = "Birth Rate",
       y = "Minimum Wage") +
  theme_minimal() +
  guides(fill = FALSE)  # Remove legend
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# Display the KDE plot
print(kde_plot)

The KDE plot vividly visualizes the negative correlation between these two variables. As we explore, we observe that higher minimum wages are associated with lower birth rates.

Hexbin Plot of Birth Rate vs. Education Enrollment

# Filter out rows with missing values in the selected variables
correlogram_data <- merged_data %>%
  filter(!is.na(`Birth Rate`) & !is.na(`Gross tertiary education enrollment (%)`))

# Create a hexbin plot
hexbin_plot <- ggplot(correlogram_data, aes(x = `Birth Rate`, y = `Gross tertiary education enrollment (%)`)) +
  geom_hex(bins = 30) +
  scale_fill_viridis_c() +
  labs(title = "Hexbin Plot of Birth Rate vs. Education Enrollment",
       x = "Birth Rate",
       y = "Gross tertiary education enrollment (%)") +
  theme_minimal()

# Convert the ggplot object to a plotly object
hexbin_plotly <- ggplotly(hexbin_plot)

# Display the interactive hexbin plot
hexbin_plotly

We shift our focus to the relationship between birth rate and gross tertiary education enrollment. The hexbin plot now comes into play, showcasing a negative correlation. Countries with higher education enrollment tend to exhibit lower birth rates.

Scatter Plot with Regression Line: Tertiary Education Enrollment vs. Minimum Wage

To add another layer to our narrative, we investigate the positive correlation between tertiary education enrollment and minimum wage. The scatter plot, accompanied by a line of best fit, illustrates this relationship.

# Scatter Plot with Line of Best Fit
# Scatter Plot with Line of Best Fit
scatter_fit_plot <- ggplot(merged_data, aes(x = `Gross tertiary education enrollment (%)`, y = `Minimum wage`)) +
  geom_point(alpha = 0.7, size = 3) +  # Added transparency and adjusted point size for better visualization
  geom_smooth(method = "lm", se = FALSE, color = "red", linetype = "dashed", size = 1) +  # Adjusted line aesthetics
  labs(title = "Scatter Plot with Regression Line: Tertiary Education Enrollment vs. Minimum Wage",
       x = "Tertiary Education Enrollment (%)",
       y = "Minimum Wage") +
  theme_minimal()  # Applied a minimal theme for clarity

# Show the scatter plot with line of best fit
scatter_fit_plot

Word Cloud Analysis: Exploring the Impact of Tertiary Education Expansion on Fertility

To complement our investigation into the socio-economic factors influencing birth rates, we turn our attention to a qualitative analysis. The following code generates a word cloud based on a research document titled “Does tertiary education expansion affect the fertility of women past the college-entry age?”.

tertiary_birth <- readLines("Downloads/tertiary_birth.rtf")

docs <- Corpus(VectorSource(tertiary_birth))
docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))
dtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df <- data.frame(word = names(words),freq=words)

set.seed(100)
wordcloud(words = df$word, freq = df$freq, min.freq = 1, 
          max.words=100, random.order=FALSE, rot.per=0.1,
          colors=brewer.pal(8, "Dark2"))

This word cloud visually represents the most prominent terms in the research document, shedding light on the key topics and themes related to the impact of tertiary education expansion on the fertility of women beyond the college-entry age. By juxtaposing these qualitative insights with our quantitative analyses, we aim to gain a comprehensive understanding of the multi-faceted dynamics influencing birth rates.

Conclusion

Our exploration into birth rates and socio-economic factors unravels significant insights. The KDE plot suggests a negative correlation between birth rates and minimum wage, while the hexbin plot highlights a similar trend with gross tertiary education enrollment. The scatter plot reinforces a positive correlation between tertiary education enrollment and minimum wage.

our comprehensive analysis underscores the nuanced relationships between birth rates and socio-economic factors. As we navigate this intricate landscape, it becomes evident that understanding birth rates requires considering the synergies and tensions among various elements, from minimum wage and education enrollment to the qualitative insights extracted from research documents. These insights contribute to a more profound comprehension of the complex forces that shape demographic trends globally

Exploring Temporal Trends: Birth, Death, and Life Expectancy Analysis

Introduction

In this section, we delve into temporal trends related to birth rates, death rates, and life expectancy. The visualizations aim to capture the dynamics of these demographic indicators over time.

Mean Life Expectancy Over Time

# Melt the data frame for better plotting
melted_data_le <- time_le_data %>%
  pivot_longer(cols = starts_with("YR"), names_to = "Year", values_to = "Life_Expectancy")

# Convert Year to numeric for proper sorting
melted_data_le$Year <- as.numeric(gsub("YR", "", melted_data_le$Year))

# Convert Life Expectancy to numeric
melted_data_le$Life_Expectancy <- as.numeric(as.character(melted_data_le$Life_Expectancy))

# Handle zero values by replacing them with NA
melted_data_le$Life_Expectancy[melted_data_le$Life_Expectancy == 0] <- NA

# Calculate mean life expectancy for each year
mean_le_data <- melted_data_le %>%
  group_by(Year) %>%
  summarise(Mean_Life_Expectancy = mean(Life_Expectancy, na.rm = TRUE))

# Create an interactive time series plot for mean life expectancy
plot_le <- plot_ly(mean_le_data, x = ~Year, y = ~Mean_Life_Expectancy,
                   type = 'scatter', mode = 'lines', line = list(width = 1))

# Customize layout
layout_le <- list(
  title = "Mean Life Expectancy Over Time",
  xaxis = list(title = "Year"),
  yaxis = list(title = "Mean Life Expectancy"),
  showlegend = FALSE
)

# Display the plot
fig_le <- plotly::layout(plot_le, layout_le)
fig_le

We start by examining the mean life expectancy over different years. The interactive time series plot visualizes how life expectancy has changed, revealing a substantial decrease in 2019. This intriguing observation prompts us to explore further.

Birth and Death Rates Over Time

Motivated by the significant drop in life expectancy in 2019, we extend our exploration to birth and death rates.

# Melt the data frame for better plotting - Birth Rates
melted_data_birth <- time_birth_data %>%
  pivot_longer(cols = starts_with("YR"), names_to = "Year", values_to = "Birth_Rate")

# Convert Year to numeric for proper sorting
melted_data_birth$Year <- as.numeric(gsub("YR", "", melted_data_birth$Year))

# Convert Birth Rate to numeric
melted_data_birth$Birth_Rate <- as.numeric(as.character(melted_data_birth$Birth_Rate))

# Calculate mean birth rate for each year
mean_birth_data <- melted_data_birth %>%
  group_by(Year) %>%
  summarise(Mean_Birth_Rate = mean(Birth_Rate, na.rm = TRUE))


# Melt the data frame for better plotting - Death Rates
melted_data_death <- time_death_data %>%
  pivot_longer(cols = starts_with("YR"), names_to = "Year", values_to = "Death_Rate")

# Convert Year to numeric for proper sorting
melted_data_death$Year <- as.numeric(gsub("YR", "", melted_data_death$Year))

# Convert Death Rate to numeric
melted_data_death$Death_Rate <- as.numeric(as.character(melted_data_death$Death_Rate))

# Calculate mean death rate for each year
mean_death_data <- melted_data_death %>%
  group_by(Year) %>%
  summarise(Mean_Death_Rate = mean(Death_Rate, na.rm = TRUE))

# Merge the mean birth and death data
merged_data2 <- merge(mean_birth_data, mean_death_data, by = "Year", all = TRUE)

# Create an interactive plot
plot_rates <- plot_ly(merged_data2, x = ~Year) %>%
  add_lines(y = ~Mean_Birth_Rate, name = "Birth Rate", line = list(color = "blue", width = 1.2)) %>%
  add_lines(y = ~Mean_Death_Rate, name = "Death Rate", line = list(color = "red", width = 1.2)) %>%
  layout(
    title = "Mean Birth and Death Rates Over Time",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Mean Rate"),
    showlegend = TRUE
  )

# Display the interactive plot
plot_rates

Our investigation unfolds a compelling narrative, marked by a consistent decline in birth rates since the turn of the millennium in 2000.

This extended decline prompts reflection on changing societal norms and evolving views towards family planning

Moreover, our exploration brings to light a noteworthy decrease in death rates, particularly evident post-2019.

Conclusion

Our journey through time reveals interesting trends. In 2019, there was a significant drop in average life expectancy. Looking closer at birth and death rates, we see a consistent decline in births since 2000 and a noticeable decrease in death rates after the 2019 pandemic.

These demographic changes suggest that external factors, like the COVID-19 pandemic, play a big role in how our population evolves. This exploration helps us understand demographic trends better and highlights the complex influences that shape our population over the years.

Interesting Visuals: Discovering Fascinating Patterns

Global Language Landscape: Top 15 Spoken Languages

Description

In this part we explores the linguistic diversity across countries by visualizing the top 15 spoken languages based on their total population.

The bar plot showcases the prevalence of each language, with the height of each bar representing the total population of countries where the language is spoken.

The numbers on top of the bars indicate the count of countries using each language, providing additional insights into the geographical distribution of linguistic communities.

This visualization offers a snapshot of the global linguistic landscape and highlights the prominence of specific languages in terms of both population and country count.

# Filter out rows where Official language is 'None'
world_data_2023_filtered <- subset(world_data_2023, `Official language` != 'None')

# Sum the population for each language
language_populations <- aggregate(Population ~ `Official language`, data = world_data_2023_filtered, sum)

# Sort the dataset by the total population for each language
language_populations <- language_populations[order(language_populations$Population, decreasing = TRUE), ]

# Select the top 15 languages
top_languages <- head(language_populations, 15)

# Count the number of countries for each language
country_counts <- aggregate(Country ~ `Official language`, data = world_data_2023_filtered, function(x) length(unique(x)))

# Merge top_languages with country_counts
top_languages_with_counts <- merge(top_languages, country_counts, by = "Official language")

# Create a bar plot with country counts and magma colors
ggplot(top_languages_with_counts, aes(x = reorder(`Official language`, -Population), y = Population, fill = `Official language`)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = Country), vjust = -0.5, size = 3, color = "black", position = position_stack(vjust = 0.5)) +  # Add text labels for country count
  labs(title = "Top 15 Spoken Languages by Total Population",
       x = "Official Language",
       y = "Total Population",
       fill = "Official Language") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  guides(fill = FALSE)

Exploring Country Frequencies by Continent

Description

This code generates an interactive donut plot displaying country frequencies by continent. The dynamic visualization not only offers insights into the distribution of countries across different continents but also allows for a more detailed exploration. By clicking on any continent, you can access information on the specific number of countries included, adding an interactive element to enhance your exploration of the dataset.

# Create a table of frequencies
frequency_table <- table(merged_data$continent)

# Convert the table to a data frame
frequency_df <- as.data.frame(frequency_table)
frequency_df$continent <- rownames(frequency_df)

# Create an interactive donut plot 
plot_ly(
  data = frequency_df,
  labels = ~Var1,
  values = ~Freq,
  type = 'pie',
  hole = 0.4,  # Set hole size for the donut effect
  textinfo = 'percent+label',
  marker = list(
    colors = scales::viridis_pal()(length(frequency_df$continent)),
    line = list(color = 'white', width = 2)
  )
) %>%
  layout(
    title = "Country Frequencies by Continent(Dynamic)",
    annotations = list(
      text = paste("Total: ", sum(frequency_df$Freq)),
      x = 0.5,
      y = 0.5,
      font = list(size = 15)
    ),
    showlegend = FALSE  
  )

Radar Chart - Multivariate Comparison by Continent

Description

The radar chart compares average values of key socio-economic indicators (unemployment rate, physicians per thousand, minimum wage, and fertility rate) across continents. It provides a visual summary of the diverse socio-economic landscapes, highlighting variations and trends among continents.

# Specify custom order for continent levels
continent_order <- c("Asia", "Europe", "Africa", "North America", "South America", "Oceania")

# Filter and aggregate data


# Calculate the mean values for each variable by continent
radar_data <- aggregate(cbind(`Unemployment rate`, 
                               `Physicians per thousand`, 
                               `Minimum wage`,
                               `Fertility Rate`) ~ continent,
                        data = merged_data, 
                        mean, 
                        na.rm = TRUE)

# Reorder the levels of the continent variable
radar_data$continent <- factor(radar_data$continent, levels = continent_order)

# Create a radar chart
ggradar(radar_data,
        grid.min = 0, 
        grid.max =5, 
        grid.mid = 2.5,
        values.radar = c("0", "2.5", "5"), # the labels of the grid
        group.point.size = 0.05,
        group.line.width = 2,
        plot.extent.x.sf = 0.9,
        plot.extent.y.sf = 1.5) +
  ggtitle("Multivariate Comparison by Continent (Average)") +
  theme(legend.position = "bottom")  # Adjust legend position

Conclusion

By looking through our radar chart we can see that, Oceania takes the spotlight, flaunting the highest minimum wage and unemployment rate, intertwining with our earlier findings on their positive correlation.

Europe steps onto the stage, boasting the highest number of physicians per thousand and, surprisingly, the least fertility rate.

Africa, standing out with the highest fertility rate, presents a unique contrast by showcasing the least rates in other variables. This unexpected twist deepens our understanding of global socio-economic dynamics.

The remaining continents contribute to the intricate mosaic, revealing similarities in the analyzed indicators.

Our exploration unfolds, weaving a captivating narrative of diversity and interconnections across the globe.